Abstract: Personalized advertising is delivered to a subject at a time when the state of mind of the subject is most receptive to the receipt of advertising such that the content of the advertising is selected based on the state of mind of the subject at the time of delivery. Activities of a subject to receive the advertisement and indicators of physiological status of the subject are monitored, and the monitored activities and indicators of physiological status are used to determine a current state of mind of the subject. The current state of mind of the subject is used to select a delivery time for the advertisement, and the content for the advertisement that is personalized to the subject is selected based on the current state of mind of the subject at the delivery time. The advertisement containing the content is communicated to the subject at the delivery time.

Abstract: Backup and restore of digital data may utilize online social network parameters of a user to determine a number of parts into which to divide digital data and distribute the parts. A list of friends from the social network friends may be selected, e.g., based on a relationship to the user in the social network, to store the parts. The parts may be stored in a friend's device memory, each of the parts stored with a different friend. The digital data may be reconstructed by obtaining a threshold number of parts from the friends.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes obtaining data to create at least one scenario for testing a conversational agent, performing a set of tests using a scenario of the at least one scenario created to assess a capability of the conversational agent, and comparing a result of the capability from the set of tests with an expected result of the scenario.

Abstract: A system, method and program product for a query-based interface in a cognitive computing platform. A system is provided that includes a query processor that inputs a query for an identified user and determines a set of results for inclusion in a cognitive response based on a set of information sources; a modality selection system that selects an interface modality for the cognitive response based on user profile data and at least one external input; and a response formatting system that determines a response format for the set of results in the cognitive response, wherein the response format is determined based on confidence levels determined from social networking information associated with the identified user.

Abstract: A user guided data mining system includes: a database storing a network graph comprising a plurality nodes and edges, node attributes for the nodes, and edge attributes for the edges; and a data mining module configured to: request feedback from a user indicating at least one of the node attributes, visualize the network graph considering the indicated attributes, request feedback from the user indicating a part of the visualized network graph and a class of graph mining algorithm, and rank a plurality of different graph mining algorithms of the indicated class based on the indicated part.

Abstract: In reply to a requester's credit request, a plurality of the requester's debtors are selected, each of which has a payment due to the requester during the repayment period. Repayment information for is retrieved for each debtor as is the requester's cash flow. A repayment probability score per debtor is computed based on the debtor's repayment history and the debtors are ranked based on the repayment probability score and an amount of the debtor's payments due. Debtors are further selected in order of rank until a total of their payment amounts at least equals a payment amount per installment. The computed repayment probability scores of the further selected debtors are aggregated into a bundle score, and a new credit score is computer for the credit request based on at least the bundle score, without reliance on information that may be used to identify any of the debtors.

Abstract: Backup and restore of digital data may utilize online social network parameters of a user to determine a number of parts into which to divide digital data and distribute the parts. A list of friends from the social network friends may be selected, e.g., based on a relationship to the user in the social network, to store the parts. The parts may be stored in a friend's device memory, each of the parts stored with a different friend. The digital data may be reconstructed by obtaining a threshold number of parts from the friends.

Abstract: Backup and restore of digital data may utilize online social network parameters of a user to determine a number of parts into which to divide digital data and distribute the parts. A list of friends from the social network friends may be selected, e.g., based on a relationship to the user in the social network, to store the parts. The parts may be stored in a friend's device memory, each of the parts stored with a different friend. The digital data may be reconstructed by obtaining a threshold number of parts from the friends.